Instructions to use rajammanabrolu/t5_supervised_en_de_wmt16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rajammanabrolu/t5_supervised_en_de_wmt16 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("rajammanabrolu/t5_supervised_en_de_wmt16") model = AutoModelForSeq2SeqLM.from_pretrained("rajammanabrolu/t5_supervised_en_de_wmt16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rajammanabrolu/t5_supervised_en_de_wmt16: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/rajammanabrolu/t5_supervised_en_de_wmt16/resolve/main/training_args.bin
- Command line
-
hf download hf://rajammanabrolu/t5_supervised_en_de_wmt16/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rajammanabrolu/t5_supervised_en_de_wmt16/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- 0858410cfadb3737f4f5947be7bf0658ce5edfea57626fd74453b3c17975e052
- Size of remote file:
- 3.18 kB
- SHA256:
- 74fbaeda740652db58450a9e0bf089571ab2c92a66cecd28a7dbd75c720be8e3
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